Why MOOCs may not help you get that Data Science Job
Many aspiring Data Scientists lament that despite doing many massive open online courses (MOOCs) they are not getting the break.
Why is that?
Because only contextual learning sticks.
There are so many interesting MOOCs out there and an aspiring data scientist feels like doing it all. Sometimes these never ending list of courses gives an aspiring data scientist a feeling of being a hamster on a wheel.
Doing MOOCs without any context would be futile as the learning would simply not stick.
Almost all the companies realize this fact and that’s why they don’t hire people who showcase ‘number of courses done’ like a vanity metric.
Also, companies prefer to hire experienced people because as part of their work they would have learnt things contextually (at least that is the assumption) ;) .
There is no point learning about convolutional neural network (CNN) when you are not going to implement one in near future anyways.
As the saying goes “we will cross that bridge when we get there”
How to learn contextually
So, if you are a fresher, how do you create a context? Rather than trying to learn everything under the s̶u̶n̶ (Machine learning or Deep Learning). Choose a sub field say NLP. Do a MOOC that teaches you the basics of NLP. Then do a project that involves applying what you learnt.
By doing this, naturally you would end up doing fewer courses but at the same time you would be closer to gaining expertise in that sub field. And the best part is you would have increased your odds of getting hired !!
So remember… Only contextual learning sticks
